System and method for detecting, profiling and benchmarking intellectual property professional practices and the liability risks associated therewith

ABSTRACT

A computer-implemented method and a computer-based system for detecting, profiling and benchmarking Intellectual Property (IP) law professional liability risks and professional liability insurance risks and value associated with IP prosecution and maintenance processes for IP business entities, including IP law firms and independent IP professionals. The invention allows further an insurance firm to accurately quantify professional liability risk of a new or existing IP business client and to mitigate such risks. The present method involves accessing and collecting transaction data indicative of risk-reducing and risk-increasing behavior of an IP business entity from a National IP Office in a chosen jurisdiction and sending the transaction data to a back-end computer system for processing and analysis. In the preferred embodiment of the invention, the transaction data is collected from the USPTO PAIR system.

CROSS REFERENCE TO RELATED APPLICATIONS

Not applicable.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to data processing systems and methodsspecially adapted for administrative, financial, managerial, supervisoryor forecasting purposes, and in particular to the area of IntellectualProperty (IP) Management. The main embodiment of the invention relatesto a computer-based method and a computer implemented system fordetecting, profiling and benchmarking IP law professional liabilityrisks and value associated with IP prosecution and maintenance process.

Description of the Related Art

IP instruments (patents, trademarks, trades secrets, industrial designs,copyrights) play an important role in our economy in encouraging privateinvestment in the development of new technologies that improveproductivity and quality of life for everyone. American innovators andbusinesses rely particularly on the legal rights associated with patentsto reap the benefits of their innovations. Timely issuance ofhigh-quality patents provides market certainty and allows businesses andinnovators to make informed, timely decisions on product and servicedevelopment. In 1993, the revenue generated from patents by U.S.companies was over $60 billion [Fred Warshofsky, The Patent Wars, JohnWiley & Sons, Inc., New York, 1994]. Today, intellectual capital andintangible assets, including technology, brands and strategiccompetencies, comprise more than 50% of the business outputs in the U.S.economy.

Patents are further valuable because they collectively represent a vasttechnological database. According to Larry Kahaner's book, CompetitiveIntelligence, Simon & Schuster, 1996, “More than 75 percent of theinformation contained in U.S. patents is never released anywhere else.”The number of patents applied for and issued to U.S. companies isincreasing every year, especially in fast moving industries such ascomputer software, telecommunications, and biotechnology. Manyinternational companies have also recognized the value of patents. Infact, foreign companies regularly rank among the leaders in issued U.S.patents.

Demand for patent examination is steadily rising: In 2008, the WorldIntellectual Property Organization (WIPO) estimated the number ofpatents in force around the world at 6.7 million, with an increasingshare of inventions being patented in more than one country. Between1991 and 2009, patent applications to the United States Patent andTrademark Office (USPTO) surged 171%, from 178,000 to 483,000. Similarsituation is occurring across the globe, contributing to larger andlarger backlogs at the world's patent offices.

Recent USPTO statistics show even further growth in patent demand. Since2010, the USPTO is receiving more than 500,000 patent applicationsannually with 629,647 applications filed for the year 2015. Theunexamined application backlog at the end of fiscal year 2016 (Sep. 30,2016) stands at 537,655 cases, and the request for continued examination(RCE) backlog increased to 27,394 cases [USPTO Performance andAccountability Report for Fiscal Year 2016].

Intellectual Property is especially crucial to the economy of the UnitedStates. IP-intensive industries directly and indirectly supported 45.5million jobs in 2014, nearly one-third of all U.S. employment. The shareof total U.S. gross domestic product (GDP) attributable to IP-intensiveindustries increased from 34.8 percent in 2010 to 38.2 percent in 2014;In addition, the average weekly wage of $1,312 was 46 percent higherthan the $896 average weekly wages in other than IP-intensive industriesin the private sector [Intellectual Property and the U.S. Economy: 2016Update, USPTO, U.S. Economics & Statistics Administration].

Intellectual property is also a big business in itself. With more than600,000 applications filed and 300,000 patent rights granted each year,patent owners and applicants pay combined annual fees of nearly abillion dollars (about $6,700 per issued patent) to the USPTO toprosecute and maintain their patents and applications. This does notinclude the additional fees and costs expended for related professionalservices, such as attorneys fees and drafting charges. In addition,thousands of patent infringement suits are brought in the federal courtseach year. A 1991 survey conducted by the American Intellectual PropertyLaw Associations (AIPLA) reported that the median cost of patentlitigation for each side through trial was about $650,000 [Report ofEconomic Survey, AIPLA, 1991]. More recent data, estimates the cost ofpatent enforcement litigation in the range of about $1 million per side.Therefore, the aggregate annual cost for obtaining, maintaining andenforcing patents in the United States is measured in billions ofdollars.

The America Invents Act (AIA), which provisions are incorporated byreference herein, raises the stakes of quality of patent prosecutioneven higher with new laws enabling individuals and firms to challengethe validity of issued patents. These post-grant challenge optionsinclude: post-grant review, inter pa-rtes (or third party) review and“covered business method” patents review, which are handled by a panelof administrative judges, highly skilled in both technology andpatent-law issues. In the new process, patents can be challenged on allgrounds, including eligibility and clarity. The inter pa-rtessubmission, allows any member of the public to submit documents andcommentary for use by patent examiners giving them access to the mostrelevant documents when examining patent applications; the submissionprocess has been greatly streamlined with no cost to the general publicfor the first three or fewer documents.

Because of great importance of patents in both the U.S. and globaleconomies there has been continued interest in quantifying the intrinsicvalue of patents and their contribution to economic prosperity of theindividuals or companies that hold or control them. Such information canbe useful for a variety of purposes. For example, patent holdersthemselves may be interested in using such information to help guidetheir future decision-making or for purposes of tax treatment, transferpricing or settlement of patent license disputes. Financial advisors andinvestors may seek to use such information for purposes of comparativevalue analysis or to construct measures of the “fundamental value” ofpublicly traded companies for purposes of evaluating possible strategicacquisitions or as a guide to investment. Economists may seek to usepatent valuations for purposes of economic forecasting and planning.Insurance carriers may use such valuations to set insurance policypremiums and the like for insuring intangible assets. However, properpatent analysis, whether for purpose of licensing, infringementenforcement, freedom to operate, technical research, productdevelopment, and others is a very difficult, tedious, time consuming,and expensive task. Accordingly, detailed patent related analysis isusually not done, or it is done in an ad hoc, unorganized, incomplete,inefficient, or ineffective manner.

Patent Prosecution

Prosecution of a patent application is a complex process extending overmany months and involving going through many stages. It starts withfiling a formal patent application in a national patent office such asthe USPTO and paying the application filing fee. To be valid, patentapplication must adhere to certain standards in accordance to USPTOrules and criteria typically comprising a specification and at least oneclaim. To define the scope of protection of the patent, the claims mustpoint out and distinctly claim the subject matter of the invention. Somedrawings are usually included in an application. They must show everyfeature of the invention specified in the claims and they need toconform to highly specific Patent Office requirements.

An examination phase consists of a series of negotiations between aPatent Office Examiner and the applicant (representative). Typically,the first Office Action rejects some or all of the claims providinggrounds for each rejection. The applicant can try to overcome therejections by amending the application or arguing why the objections areincorrect. The second Office Action, made after the Examiner considersthe response and amendments submitted by the applicant, will typicallyend in an allowance or a final rejection of some or all of the claims.The Applicant can in turn adopt the changes as suggested by theExaminer, appeal the Examiner's decision, abandon the application if allof the claims have been rejected, or file a continuation application.

After all of the Examiner's objections are met and the application meetsall other requirements, a Notice of Allowance is issued, specifying theissue and publication fees that must be paid prior to the patent beingissued. Utility and reissue patents are issued within about four weeksafter the required fees have been received by the Patent Office. Apatent number and issue date will be assigned to an application and anIssue Notification will be mailed after the issue fee has been paid andprocessed by the USPTO. Patent Grant document is mailed on the patent'sdate of issuance.

Maintenance Fees

Maintenance fees for every U.S. granted patent are due at 3½, 7½, and11½ years after the grant date and have to be paid on time in order tokeep the patent from being abandoned. In the majority of foreigncountries, fees are due annually even if the patent is still in theapplication phase. Payment of maintenance fees, in particular theforeign annuities, requires interaction between a docketing application,the law firm, the client, the annuity payment service firm, the foreignassociate and the Foreign Patent Office. Due to the inefficiency of thisprocess, clients must typically make decisions whether to pay theannuity fees to maintain the applications/patents in good standing fourto six months prior to the actual due date.

In some cases patent assets are intentionally abandoned by their ownersbecause they are perceived to have little or no remaining value, orkeeping the assets in good standing is no longer economically sound. Inother cases, the IP will lapse due to inattention, mistake, orprofessional negligence. Particularly troublesome are periods of changesuch as merger of businesses, an acquisition of a business, adivestiture of a business, or sale/purchase of a new patent asset. Toassist patent owners, the USPTO Official Gazette publishes a prospectivelist of patent numbers which will require payment in an upcoming period.Patent owners can “revive” patents which have become abandoned due tolack of maintenance fee payments, however they must prove that theabandonment was unintentional or unavoidable and pay an extra petitionfee. Furthermore, in some cases the petition must be filed within 2years of the abandonment.

In cases of alleged professional negligence, the owner of an IP assetmay seek to be compensated by the service provider, a law firm forexample, that made a docketing mistake or failed to issue the correctinstructions, proportionately to the value of the forfeited IP asset.This can include compensation for the cost, time and effort to attemptto revive the IP asset or the asset's value itself, if it cannot berevived. Even if the revival is successful, the owner may seekcompensation for “diminished value” of the asset due to “interveningrights” of a third party.

IP Attorneys and Patent Agents

Filing a patent application does not automatically guarantee receivingpatent rights. Latest research suggest that, based on prosecutionhistories of 2.15 million U.S. patent applications from 1996 tomid-2013, only 55.8% of the applications emerged as patents withoutusing continuation procedures to create related applications. Theallowance rate has decreased substantially over time, particularly forapplications in the “Drugs and Medical Instruments” and “Computers andCommunications” fields. Furthermore, applications filed by small firmswere less likely to emerge as patents than those filed by large firms.[What is the Probability of Receiving a U.S. Patent?, Michael Carley,Deepak Hegde, and Alan Marco, 17 YALE J.L. & TECH. 203, 2015].

There are many reasons for the above statistics including complexity ofthe patenting process itself. Filing and prosecuting patent applicationin any national Patent Office is an undertaking requiring substantialknowledge of IP laws and Office practices and procedures, as well asconsiderable knowledge of the scientific or technical matters involvedin the particular invention. While patent rights may be obtained in manycases by persons not skilled in those areas, there is no assurance thatthe obtained patent would adequately protect the particular invention.

Most inventors employ the services of registered patent attorneys orpatent agents. The USPTO maintains a register of attorneys and agentspermitted by law to represent inventors before the USPTO. To beadmitted, an applicant must possess the legal, scientific, and technicalqualifications necessary to render a valuable service and pass strictexamination process, which requires a college degree in engineering orphysical science or the equivalent of such a degree. The USPTO registersboth attorneys at law and persons who are not attorneys at law. Theformer persons are referred to as “patent attorneys,” and the latterpersons are referred to as “patent agents.”

The USPTO cannot recommend or aid in the selection of any particularattorney or agent or respond to any inquiries about “reliability” or“professional capability” of any patent attorney, agent, or firm listedon the USPTO register. The fees charged to inventors by patent attorneysand agents for their professional services are also not subject toregulation by the USPTO. Definite evidence of overcharging maytheoretically form a base for some USPTO action, but in reality theOffice rarely intervenes in disputes concerning fees. The USPTO willhowever act upon complaints against attorneys and agents guilty of grossmisconduct or lack of professional qualifications with the power todisbar or suspend them from practicing in their jurisdiction.

Professional Liability Risks

The procurement of patent rights is one of the most challenging IPprocesses in view of the complex and ever changing IP laws and PatentOffice governing practices. This holds especially true in case of patentapplications filed under the PCT and entering National/Regional stage ofprosecution in multiple National Patent Offices. In such case,Applicants are faced with a landscape of national-based variations informal or procedural requirements. There is no centralized databaseproviding easy access to deadline correspondence, such as Office Actionsfor all the regional or national jurisdictions. In some cases, due tomissed deadlines or formal requirements, mistakes can lead toirrevocable loss of patent rights. Even the effort necessary to maintainup-to-date forms and keep up with ever changing procedures can be verytaxing, particularly for smaller IP firms, independent IP professionals,and foreign associates.

As with patents, trademarks are also subject to examination, misseddeadlines, incorrect or invalid priority claims or incompleteapplications and risk events that could potentially affect the liabilityof IP firms and IP professionals and the rights of their owners.

The trademark prosecution process is government sanctioned and eachapplication undergoes rigorous examination by the relevant National IPOffice. Trademark application filed by way of national applicationinvolves sending of instructions between counsel in two differentjurisdictions. The process of documenting and filing a nationaltrademark applications will usually be based on a prior correspondingforeign application or registration. This process of obtaining andinputting the information from one national filing system into anotheris human managed, manual, and error prone.

The inability of users and trademark owners to detect filing mistakes,some of which are time sensitive and not correctable after a deadline,highlights the risk associated with correctly inputting foreign-basedinformation. By the same token, the trademark examination processdisplays characteristics of event risk such as an attorney having misseda deadline and unsuccessfully seeking to revive an abandoned applicationafter the abandonment period has expired or the difficult to explainhigh ratio of abandonments for failure to respond to notice of approval.

There is a significant shortage of IP specialists in view of the demandthat has greatly increased the costs of hiring IP professionals. Inaddition, the average time required to prepare and file an applicationis longer than ever due to the increased workloads on the agents andattorneys. To reduce delays, certain patent professionals have culledtheir client base to focus exclusively on serving large or well-fundedcorporate clients or to specialize in narrow areas of technology.Others, try to cope with the increase workload by drastically shorteningprior art searches, cutting the time for specification drafting, orallotting less time to properly match the scope of claims to theinvention. Moreover, since every application is unique, there is alearning curve to it, and the process can become lengthy, expensive, andprone to errors.

Historically, cases of professional negligence, errors or omissionsregarding IP work have been rather difficult if not impossible to trackdue to the complex nature of patent prosecution and limited access toPatent Office records. Furthermore, the USPTO does not providestatistical data for cases of incomplete applications or documents notadhering to the USPTO standards and requirements.

The situation changed dramatically since 2011 with introduction of thePatent Application Information Retrieval (PAIR) system that can beaccessed over Internet-based portal via Public or Private PAIR channels.Through the former, general public can check current status of issued orpublished patent applications and obtain image file wrappers (filehistories) of published applications including direct access toprosecution documents. Private PAIR allows registered users to accessstatus of their unpublished patent application and to track progress ofthe prosecution process.

The PAIR system has been in operation for several years but still hassome limitations. Knowledge of a specific case number is required forexample to access data and the output results are not easy to review.There are access limiting mechanisms put in place by the USPTO,including CAPTCHA test and timeout limits, to prevent machine access andlimit the amount of traffic.

PAIR data is grouped and accessible via multiple tabs, includingApplication Data, Transaction History, Image File Wrapper, ForeignPriority, Published Documents, Address & Attorney/Agent, SupplementalContent, and Assignments. While the tabs are reasonably well organized,the site is difficult to navigate within a conventional browser becausemany of the conventional function buttons do not operate in a consistentmanner, resulting in resubmitting the query all over again. In addition,the system frequently times out and forces the user to go through theCAPTCHA test again, which is both frustrating and time consuming.

Notwithstanding PAIR limitations, the system allows for a remote accessand downloading of a broad range of accessible IP prosecution data thatcould be used for variety of applications, including a qualitativeanalysis and benchmarking of professional IP practices and liabilityrisks associated therewith that is the purpose of the present invention.

At present, the industry for ranking intellectual property professionalsand law firms is scattered with multiple players and disparatemethodologies for ranking the performance and quality of the work done.Some of the website-based rankings of general law and IP law firms canbe accessed via the following links: www.managingip.com,www.IPStars.com, www.IAM-media.com, www.IPwatchdog, www.Juristat.com,www.ificlaims.com, www.vault.com, www.legal500.com,www.bestlawfirms.com, www.lexpert.ca, to name just a few. Most of thesesites focuses on the U.S. market but some include international IPrankings for selective multiple IP jurisdictions as well.

Such performance rankings of intellectual property law providers areusually based on a subjective survey of peers or clients and reputationof those clients, number of applications filed, portfolio size, theextend of time to take an application from filing to issuance, number ofclaims approved and/or number of office actions processed.

Purchasers of intellectual property law services, financiers, potentialemployers and others use these rankings as factors to make decisions onwhether or not to invite, consider, or retain a particular serviceprovider. In turn, providers of these services rely on these rankings asproof of their competence and reliability. However, empirically, theserankings have a limited scope and reliability.

First, there is no objective validation of the results of existingrankings. Second, they fail to address in particular the uniquebehavioral profile of each firm. Third, none of the rankings relyexclusively on an empirical methods and systems. Fourth, there is nomethod or system for IP law providers that utilizes independentbenchmarks to assess the quality of their work against that of theirpeers and competitors.

Professional Insurance Liability Risks

The assessment of and the decision to provide professional liabilityinsurance coverage of intellectual property law firms has been dependentup to now on law firms' voluntary disclosure of their past risk to theirinsurers, the types of risk management practices they employ, thesystems and people in place to manage those risks, and their continuingvoluntary obligation to disclose any risk that may affect theirinsurance coverage.

To asses risks involved with a particular law firm policyholder, theinsurance industry usually relies on infrastructure requirements asdescribed above and the claims history profile, if any. While insurersand brokers have access to past claims data as a basis to assess andpredict risk, the tools to assess these risks are subjective andlimited.

First, insurers and brokers have access to reported data belonging onlyto their clients; There is no publicly accessible empirical practiceprofile to the IP industry. Second the data available to the insuranceindustry is limited to claims data and mostly claims driven. There is nosystem to access and determine behavioral activities of current andpotential policyholders. Third, aside from the generally acceptableinsurance risk principles, the IP professional liability insuranceindustry and each insurer relies only on subjective criteria.

There is currently no method or system to determine and assess empiricalbehavioral non-claims based data, including reportable incidents ofpolicyholder professional activities before a patent and trademarkoffice, without having to continuously ask and inspect a policyholder'sfile.

The lack of objective assessment of a law firm's empirical behavioralnon-claims based data and reportable incidents of professionalactivities exposes a significant black hole in the risk assessment of acurrent and potential policyholder. This black hole in turn affects theability of insurers and brokers to reliably review the current, andproperly predict the future risk behavior of a policyholder. In turnthis black hole also reduces the ability for proper assessment of a riskprofile of a client and reduces the potential for a better pricepremium.

There are several prior art publications disclosing system and methodsfor accessing the PAIR system data for a variety of applications,including analyzing the prosecution documents, however all of them focuson quantitative analysis of the data rather than on qualitativeassessment indicative of a professional liability risks associated withIP prosecution and maintenance.

The U.S. Pat. No. 9,305,278 “System and Method for CompilingIntellectual Property Asset Data”, incorporated by reference herein,describes a method for operating an access computing system forretrieval of data from a target organization database with limited orchallenging access via different and improved interface. It furtherprovides for a system indirectly analyzing and predicting behavior of atarget organization, including profiling and characterizing the behaviorof art units and/or specific personnel, which is an IPO, across anentire set of documents. Therefore, the focus of the above method andsystem is on obtaining the data and profiling an IPO rather thanperformance of IP professionals and IP law firms.

The U.S. Pat. No. 9,183,278 “Computerized Information System forCreating Patent Data Summaries and Method Therefor,” incorporated byreference herein, describes a method and a computer system forgenerating comprehensive patent data summary reports on a target entitybased on searching and identifying all publicly available patent recordsassociated with that entity. In one of the embodiments, the above systemdetermines number of issued patents for each patent attorney associatedwith a business entity and ranks them by number of patents associatedwith the business entity. However, there is no intention of ranking lawfirms or patent attorneys based on the quality of their work andapplying such data to a professional liability risk analysis, scoringtheir work, risk benchmarking and underwriting liability insurancepolicy making.

The U.S. published patent application No. 2015/0348217 “Patent Rating”,incorporated by reference herein, proposes a system and method forassigning a rating to a patent owned by a business entity based on aselected aggregated metric modeled on patent prosecution data. Thesystem is clearly meant to rate patent documents rather than performanceof patent professionals or to help underwriters and insurance companiesask underwriting risk.

The U.S. published patent application No. 2016/0189084 “System andMethod for Determining the Value of Participants in an Ecosystem to OneAnother and to Others Based on Their Reputation and Performance”,incorporated by reference herein, describes system and method for ratingparticipants in an ecosystem based on their past performance andreputation index based on feedback from other participants in thesystem. However, the described method and system applies towards mediaindustries (video, film, TV) rather than insurance and IP laws andpractice with no provisions at all for rating IP-related professionalliability risks.

Therefore, in light of the limitations and disadvantages of the priorart, the present invention addresses the need for a comprehensive methodand a system for analyzing the historical and real time IP prosecutionand administrative data to access a professional liability risks of anIP business entity, including law firms and IP professionals, inclusiveof scoring and ranking those entities against empirically calculatedbenchmarks. There is also need to assess further the professionalliability insurance risk profile of those entities, which would beuseful to commercial insurance carriers for setting insurance policypremiums and mitigating their risks associated with liability coveragepolicies of their actual and potential clients.

SUMMARY OF THE INVENTION

The present invention is directed to the Patent Practice Benchmark (PPB)computer-implemented method and the PPB computer-based system fordetecting, profiling and benchmarking IP law professional liabilityrisks and professional liability insurance risks and value associatedwith IP prosecution and maintenance process of an IP Target Entity,including IP law firms and independent IP professionals. The inventionallows further an insurance firm to accurately quantify professionalliability risk of a new or existing client (Target Entity) and tomitigate such risks. The invention allows further any party, includingbusiness entities, nonprofit entities, universities, R&D Institutes,commercial clients, vendors, marketers, etc. (Client User) to obtain apatent practice score of any patent law firm. PPB accesses and collectstransaction data indicative of risk-reducing and risk-increasingbehavior of an IP Target Entity from a National/Regional IP Office (IPO)in a chosen jurisdiction and sends the transaction data (Asset Data) toa back-end system for processing and analysis.

In the preferred embodiment of the invention, the IPO is the USPTO andone of the transaction data sources is the PAIR system hosted by theUSPTO.

Forming one aspect of the invention is the PPB computer-based systemapparatus capable of obtaining and analyzing information downloaded fromthe IPO to determine whether the information is indicative of IP lawprofessional liability risk-reducing or risk-increasing behavior.

Forming another aspect of the invention is the PPB computer-implementedmethod for analysis of the information whereby, the back-end systemdetermines whether the received information is indicative of arisk-reducing or risk-increasing behavior based on a threshold factor(benchmark) established during the process.

According to another aspect of the invention, the PPB computer basedsystem apparatus can be coupled to another computing device, whichprovides real time data about the risk-reducing or risk-increasingactivity of an insured entity before any IPO.

According to another aspect of the invention, the device can record anyperiods in which the insured entity has any risk-reducing orrisk-increasing behavior before the IPO.

Another aspect of the invention is that upon confirmation that theinformation indicates risk-reducing or risk-increasing behavior, theback-end system may perform one or more actions defined by a set ofrules to establish a trigger alert to the insurer, broker or the insuredentity for a response action corresponding to the behavior.

Additional advantages and/or features of the present invention will beset forth in the following detailed description. It is to be understoodthat both the foregoing general description and the following detaileddescription of the present invention are exemplary and explanatory andshould be treated broadly as to not limit the scope of the invention asclaimed.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present disclosure will now be described, by way ofexample only, with reference to the attached figures.

FIG. 1 is a is a block diagram of the PPB computer-based system,according to an exemplary embodiment of the present invention.

FIG. 2 is an overview of the PPB method, according to an exemplaryembodiment of the present invention.

FIG. 3 shows the Data Update Process, according to an exemplaryembodiment of the PPB method.

FIG. 4 is a block diagram of the Patent Practitioner/Law Firm DataIndexing and Consolidating, according to an exemplary embodiment of thePPB method.

FIG. 5A is a block diagram of the Transaction History Data Preparationstage, according to an exemplary embodiment of the PPB method.

FIG. 5B is a block diagram of the Maintenance Fees Data Preparationstage, according to an exemplary embodiment of the PPB method.

FIG. 5C is a block diagram of the Subject Matter Professional Conflictof Interest Data Preparation stage, according to an exemplary embodimentof the PPB method.

FIG. 6A is a block diagram of the Transaction History Risk Calculationstage, according to an exemplary embodiment of the PPB method.

FIG. 6B is a block diagram of the Maintenance Fees Risk Calculationstage, according to an exemplary embodiment of the PPB method.

FIG. 6C is a block diagram of the Subject Matter Professional Conflictof Interest Risk Calculation stage, according to an exemplary embodimentof the PPB method.

FIG. 7 is a block diagram of the Risk Data Consolidation stage and theBloomberg GIC data grouping, according to an exemplary embodiment of thePPB method.

FIG. 8 is a block diagram of the Advanced Calculations stage, accordingto an exemplary embodiment of the PPB method.

FIG. 9 is an exemplary layout of a user-dependant GUI of the PPB system,according to an exemplary embodiment of the present invention.

FIG. 10 is a schematic view of a network environment within which thePPB computer-based system operates, according to an exemplary embodimentof the present invention.

DETAILED DESCRIPTION

The presently disclosed embodiments of this invention are directed to acomputer-implemented method and computer-based system for detecting,profiling and benchmarking IP professional practices and the liabilityrisks associated therewith.

It is to be understood that both the foregoing and the following detaildescription are exemplary and explanatory only and are not intended tolimit the claimed invention in any manner whatsoever. Throughout much ofthe description, reference is made to patents and deadlines applicableto patent prosecution. However, it should be understood that the presentapplication is also applicable to other forms of IP, such as trademarks,industrial designs, copyrights, etc. and to all types of deadlines, notjust reply deadlines to refusals.

As used herein, the term “intellectual property” or “IP” relates to thevarious forms of intellectual property, including patents, trademarks,trade secrets, industrial designs, copyrights, trade dress, plantbreeders' rights, but in particular to patents and trademarks. It isintended that the term should have a broad definition includingtechnology embodying the IP (e.g. hardware, software, computer programsand systems, training methods, methods of doing business), as well asthe know-how and methods for using the IP.

As used herein, the term “National/Regional IP Office” or “IPO” refersto a governmental or intergovernmental organization that controls theprocess of issuing or registering intellectual property, such as patentsand trademarks. Examples of IP Offices include the United States Patentand Trademark Office (USPTO), the Canadian Intellection Property Office(CIPO), World Intellectual Property Organization (WIPO), EuropeanIntellectual Property Office (EUIPO), European Patent Office (EPO),Office for Harmonization in the Internal Market (OHIM), United KingdomIP Office (UKIPO), Japan Patent Office (JPO), and others.

As used herein, the terms “agent,” “patent agent,” “patent attorney,”“patent practitioner” refer to a person authorized to act before one ormore IP Offices in respect to IP matters. The persons might performIP-related professional work in their own capacity or as an employees ofIP law firm or other entities that are authorized to act before therespective IPOs.

As used herein, the term “target entity” refers to a natural personand/or corporate entity (business person) engaged professionally in anyIP-related work, including IP law firm and IP professionals employed bythe law firm, in-house IP departments, corporate IP counselors,self-employed IP attorneys, agents, professionals and such.

As used herein, the term “general client” may include any individual,consumer, consumer group, business entity, organization, governmententity, transaction account issuer or processor (e.g. credit orfinancial institution, etc.), merchant, consortium of merchants,consumer, account holder, charitable organization, and/or any othersimilar entity.

As used herein, the term “liability risk profile of a target entity”means a profile based on any historic and recent professional activitiesrelated to IP portfolio asset prosecution, litigation or management,including events of professional practice errors, omissions, events ofincompleteness or mismanagement, associated with the target entityincluding professional employees of the target entity.

As used herein, the term “liability insurance risk profile of a targetentity” means a risk assessment profile allowing insurance companiesoffering professional liability insurance coverage with respect to atarget, agent and/or its professional employees to assess insurancerisks, determine the amount of coverage needed, as well as how much thatcoverage should cost and manage their exposure and mitigate potentialclaims associated with a target entity including its professionalemployees for both current and potential new clients. Such a riskprofile provides visibility based on historical data into a current orprospective client's ability to handle past and future IP relatedtransactions thereby enabling insurance companies to quantify a target'sorganization's exposure and risks.

As used herein, the term “liability trigger events” means any historicand recent professional activities related to IP portfolio assetprosecution, litigation or management, including events of professionalpractice errors, omissions, events of incompleteness or mismanagement,associated with the target entity including its professional employees,that are susceptible to effect the liability risk profile and theliability insurance risk profile of a target entity.

As used herein, the term “Asset Data” refers to any data related to IPprosecution, litigation, and administration including historic and realtime IP prosecution transaction data, IP administrative data,maintenance fees data, reexamination and reissue data, litigation andappeals data, assignments data, and in particular USPTO PAIR TransactionHistory data.

As used herein, the term “Liability Alert Data” means Asset Dataprofiled for risk event/action codes associated with one or more ofliability trigger events.

As used herein, the term “predetermined factors” means a set ofliability trigger events that are considered to be indicators of a risk.

As used herein, the term “Weighted Liability Alert Data” means LiabilityAlert Data associated with predetermined factors associated with theseriousness of a “liability trigger event”.

As used herein, the term “predictive models” means a process of datamining combined with probability analysis to forecast outcomes of an IPprocess or event.

As used herein, the term “Subject Matter Professional Conflict ofInterest” or “Conflict of Interest” or “Conflict” means situations wherethere is concurrent patent representation by the same practitioner fordifferent applicants or patent holders in the same area of technology.

As used herein, the term “IP classes” or “classes” means classes andsubclasses as cited on the published patent application/grant documentsaccording to the Cooperative Patent Classification System (CPP), theUnited States Patent Classification System (USPC), and the InternationalPatent Classification system (IPC), including the Field ofClassification Search classes and subclasses.

As used herein, the term “liability risk score” or “risk score” meansthe likelihood or calculated probability that a “target entity” willface or be the cause of an error that would result in a professionalliability claim.

As used herein, the term “liability insurance risk score” or “insurancerisk score” means the score that is used to assess factors as to whetheran entity should be subject of insurance coverage or term thereof.

As used herein, the term “IP document” or “IP case” refers to any IPapplication or IP grant. Examples include patent and trademarkapplications, granted/issued patents and trademarks, and others.

As used herein, the term “Master Patent Practitioner Roster” or “MPPR”means for instance USPTO Patent Practitioner Roster List of agents andattorneys registered to practice with the USPTO or the equivalent IPObody compared with a list of agents and attorneys found on allapplications and grants before the IPO indexed by location and size.

As used herein, the term “Master Law Firm Roster” or “MLFR” means aroster of all IP law firms listed with agents and attorneys in theMaster Patent Practitioner Roster” or “MPPR.

As used herein, the terms “MPPR-Loc” and MLFR-Loc” mean MPPR and MLFRtables indexed for the number of geographical single/multi officelocations.

As used herein, the term MLFR-Size” means MLFR table indexed for thenumber of IP professionals employed by a roster of law firms and sortedby the law firm.

As used herein, the term “PP/LF Assets” means a consolidated table ofall IP law firms associated with their IP professional employees andwith their active IP portfolio cases (patent applications/grants).

As used herein, the term “Market Portfolio and TH Risk Events Table” or“MP&TH RET” means the “PP/LF-Assets” data consolidated with theTransaction History risk event/action codes sorted by year.

As used herein, the term “Market Portfolio and MF Risk Events Table” or“MP&MF RET” means the “PP/LF-Assets” data consolidated with theMaintenance Fees event/action codes sorted by year.

As used herein, the term “Market Portfolio and CI Risk Events Table” or“MP&CI RET” means the PP/LF-Assets data consolidated with the SubjectMatter Professional Conflict of Interest risk event counts sorted byyear.

As used herein, the term “TH score” or “THS” means a score calculated onan annual bases by the PPB method to reflect the risk liability of atarget entity (IP law firm or patent practitioner) based solely on itsportfolio Transaction History risk event/action codes.

As used herein, the term “TH Score Average” or “THSA” means the averageof the TH score (THS).

As used herein, the term “TH Benchmark” or “THB” means the standardagainst which the overall market is measured against based on theTransaction History risk event/action codes alone.

As used herein, the term “TH Benchmark Average” or “THBR” means theaverage score of the TH benchmark (THB).

As used herein, the term “MF Score” or “MFS” means a score calculated onannual bases by the PPB method to reflect the risk liability of a targetentity (IP law firm or patent practitioner) based solely on itsportfolio Maintenance Fee Event codes.

As used herein, the term “MF Score Average” or “MFSA” means the averageof the MF Score (MFS).

As used herein, the term “MF Benchmark” or “MFB” means the standardagainst which the overall market is measured against based on theMaintenance Fee Event codes alone.

As used herein, the term “MF Average Benchmark” or “MFAB” means theaverage of the MF benchmark (MFB).

As used herein, the term “CI Score” or “CIS” means a score calculated onannual bases by the PPB method to reflect the Subject MatterProfessional Conflict of Interest (CI) risk liability of a target entity(IP Law Firm or Patent Practitioner) based solely on its portfoliocounts of Potential Conflict Events.

As used herein, the term “CI Score Average” or “CISA” means the averageof the CI score (CIS).

As used herein, the term “CI Benchmark” or “CIB” means the standardagainst which the overall market is measured against and based on the CIrisk event counts alone.

As used herein, the term “CI Benchmark Average” or “CIBA” means theaverage of the CI benchmark (CIB).

As used herein, the term “Combined Risk Profile Table” or “CRPT” meansTH, MF, and CI risk events table consolidated with patentpractitioner/law firm portfolios sorted by years and indexed by avariety of risk factors.

As used herein, the term “GIC Risk Table” or “GICRT” means a tabulationof risk events pre-sorted by BIC Bloomberg business codes denoting areasof business.

As used herein, the term “Overall Market Score” or “OMS” means thecombined risk events of the entire market of IP practice events.

As used herein, the terms “Overall Market Average” or “OMA” means theaverage of the Overall Market Score divided by the total number of IPlaw firms.

As used herein, the term “Patent Practice Benchmark” or “PPB” means agroup of law firms whose risk scores are consistently above the OverallMarket Score (OMS) and Overall Market Average OMA).

A detailed overview of the PPB computer-based system and method of thepresent invention will now be described with reference to FIG. 1 to FIG.10.

For the sake of brevity, conventional data networking, applicationdevelopment and other functional aspects of the PPB systems, includingindividual operating components of the system, may not be described infull detail herein. Furthermore, the connecting lines shown in thevarious figures contained herein are intended to represent exemplaryfunctional relationships between the various elements. Many other oradditional functional relationships or physical connections may bepresent in a practical implementation of the PPB system.

While the description references specific technologies, hardware,equipment, system architectures and data management techniques,practitioners will appreciate that this is just one potential embodimentof the PPB system and method and that other devices and/or processes maybe implemented without departing from the scope of the invention.Similarly, while the description may reference a user interfacing withthe system via a personal computer, other interfaces may include mobiledevices and handheld devices such as personal digital assistants.

An exemplary embodiment of the present invention is now described withreference to FIG. 10, which shows a network environment within which thePPB computer-based system 100 and PPB method 200 operates. There is thePPB computer-based system 100 connected to the Network 130 and includinga Host Server 118, mass data storage device (DSD) 106, a user interface109, a System Administrator Terminal 119, and a cloud-bases data storage120. The logical units of PPB System 100 may reside on a single HostServer 118 or be implemented across multiple servers (e.g. web server,file server, database management server) cooperating with each other byway of a pooled, distributed, or redundant computing model.

In alternative embodiments, the Host Server 118 operates as a standalonedevice or may be connected (e.g., networked) to other computer devices.In a networked deployment, the Host Server 118 may operate in thecapacity of a server or a client device in server-client networkenvironment, or as a peer device in a peer-to-peer (or distributed)network environment. The Host Server 118 may be a personal computer(PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant(PDA), or any other device capable of executing instructions (sequentialor otherwise) that specify actions to be taken by that device. Further,while only a single Host Server 118 is illustrated, the term “device”shall also be taken to include any collection of devices thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

The User Interface 109 allows one or more users to access the PPB System100 via User Terminals 112 upon creating an account and log in. UserTerminals 112 may be a personal computer, a mobile device, or any othercommunication or computing device capable of communicating and accessingthe User Interface 109 via a Network 130. The User Interface 109 may behosted on a Web Server (e.g. Microsoft Azure) and implemented using avariety of programming languages or programming methods, such as HTML(HyperText Markup Language), VBScript (Visual Basic® Scripting Edition),JavaScript™, XML® (Extensible Markup Language), XSLT™ (ExtensibleStylesheet Language Transformations), AJAX (Asynchronous JavaScript andXML), Java™, JFC (Java™ Foundation Classes), and Swing (an ApplicationProgramming Interface for Java™). The PPB System 100 can be alsoaccessed by a System Administrator 119 via the same User Interface 109with an administrator level log in or directly on a local level.

Network 130 may include local-area networks (LAN), wide-area networks(WAN), combinations of LAN's or WAN's, such as the Internet which is thepreferred option for this invention, wireless networks (e.g., 802.11 orcellular network), the Public Switched Telephone Network (PSTN) network,personal area networks (e.g., Bluetooth) or other combinations orpermutations of network protocols and network types. Devices and systemsof the User Terminal 112 may be coupled to Network 130 via one or morewired or wireless connections.

The PPB System 100 has access via Network 130 to at least oneNational/Regional IP Office (IPO) System hosting IP-related data andinformation related to IP prosecution and administration in thatparticular IPO jurisdiction (e.g. patent or trademarks applications,issued patent grants, registered trademarks). Some of the IPOs targetedby the PPB System 100 might include the USPTO 303, WIPO 912, EPO 913,and CIPO 914 as shown in FIG. 10. It is presumed that each of the IPOsis connected to the Network and accessible to the PPB System 100 via anE-access Interface hosted by the IPOs (not shown in detail here). TheE-access Interface allows the PPB System 100 to access and downloadIP-related data (Asset Data) from the IPO systems.

FIG. 1 shows the logical components of the exemplary PPB computer-basedsystem 100 comprising Data Acquisition & Clustering Engine 101, DataFiltering Engine 102, Data Indexing & Consolidating Engine 103, DataWeighting Engine 104, Estimator Engine 105, Local Data Storage Device(DSD-Local) 106, Data Comparison Engine 107, Search Engine 108, Userinterface 109, Communication Means 110, and Cloud Data Storage Device(DSD-Cloud) 120.

The Data Acquisition & Clustering Engine 101 uses Communication Means110 to access, obtain, and download IP-related data (Asset Data) from anIPO System of choice 912-915. The downloaded Asset Data can be stored ona local data storage devices (DSD) 106 or in a cloud. The Data FilteringEngine 102 filters and profiles the downloaded Asset Data checking forrisk event/action codes associated with one or more liability triggerevents and by other factors as required by the PPB method 200. The DataIndexing & Consolidating Engine 103 is used for indexing andconsolidating Law Firms with their IP practitioner employees, with theirIP portfolios, and with a variety of consolidated risk tables asrequired by the PPB method 200.

The Data Weighting Engine 104 applies one or more predetermined factorsassociated with the liability trigger events to the Risk Alert Data toproduce Weighted Risk Alert Data, which is stored to the local massstorage device 106. The Estimator Engine 105 uses one or more processorsto determine a variety of factors associated with a pre-selected lawfirm, including the number of professional employees on staff, thenumber of IP transaction conducted, and the dollar amount of filing feespaid by in a pre-determined period of time.

The Comparison Engine 107 uses one or more processors to determineliability risk profile of a pre-selected law firm by applying one ormore predictive models trained on the Asset Data to the Weighted RiskAlert Data calculating a variety of risk factors including risk score,risk rankings, and benchmarks to be outputted to the local DSD 106, to aUser Interface 109, and to range of display devices. Those risks includebut are not limited to risks related to Transaction History Data (THD),Maintenance Fee Events Data (MFD), and Professional Subject MatterConflict of Interest Data (CID).

The Search Engine 108 is used to respond to a user request for riskprofiles of a pre-selected law firm inputted via the User Interface 109and for other information as allowed by the user log in privileges. TheUser interface 109 allows the users to access the PPB System 100 andinput their requests for risk profile of pre-selected law firm or forother information as allowed by the user log in privileges. TheCommunication Means 110 allow the PPB System 100 to communicate with theIPOs via the Network 130 and with the users via the User Interface 109.The Local Data Storage Device (DSD-Local) 106 is used to store thedownloaded Asset Data as well as all intermediary and final results ofthe PPB method 200. The cloud-based Data Storage Device (DSD-Cloud) 120is used as a mirror backup for all of the local databases and tofacilitate and speed up delivery of some of the PPB method 200 resultsto the users. The DSD-Cloud 120 may be hosted on proprietary Web Serveror a shared cloud-based server space rented from Microsoft Azure, as anexample.

The following description relates now to the PPB computer-implementedmethod 200 for detecting, profiling and benchmarking intellectualproperty (IP) trigger events indicative of performance quality andsubject matter conflict of interest of IP professional practices andsusceptible to affect liability risk profile and professional liabilityrisk profile of a target entity, including IP professionals employed orrepresenting the target entity.

The PPB method 200 calls for accessing and downloading an IP-relateddata (Asset Data) from an IPO system to a local databases, filtering thedownloaded data by checking for codes associated with liability triggerevents to produce Liability Alert Data (LAD), weighting the LAD withpre-determined weights indicative of a seriousness of a given code toproduce a Weighted Liability Alert Data (WLAD), and cross-checking WLADagainst combined patent practitioners/law firm portfolios to determinethe liability risk profile and the liability insurance risk profile of apatent practitioner or a law firm on an annual bases. Upon user'srequest, the pre-calculated markers of the liability risk profiles (riskscore, risk ranking, industry benchmark) for a pre-selected patentpractitioner or a law firm could be displayed on user's device of choicevia the User Interface 109.

At consecutive stages of the PPB method 200, various sets of data areprocessed, filtered, consolidated and stored in their respectivedatabases or tables that are often compared against each other. Forexample, data stored in a first database may be associated with data ina second database through the use of common data fields. In variousembodiments, a given field data may be associated with more than oneportfolio by creating multiple entries in the portfolio database, onefor each portfolio the data field is associated with. In otherembodiments, one or more patent reference documents may be associatedwith a patent by creating multiple entries in the patent's database.

The databases may be composed of one or more logical or physicaldatabases. Operations database, holding computer executable statementsand instructions, may be implemented as a relational database, acentralized database, a distributed database, an object orienteddatabase, or a flat database in various embodiments. The structure,format and titles of the databases used in this description are forillustration purposes only and other structures, names, and formats maybe used as required by other embodiments of the invention. Additionally,further associations between data stored in the databases may be createdas discussed further herein.

The description set forth will be directed now to an exemplaryembodiment of the PPB method 200 directed to the USPTO and to patentprosecution only. A person skilled in the art pertaining to thisinvention will appreciate the fact that the same system and method maybe used in relation to any other National or Regional IPO for scoringand benchmarking IP practitioners and law firms practicing under theirjurisdiction and to prosecution of any other IP assets (trademarks,industrial designs, etc.) as long as the transaction history data isavailable for public access. The sources of the IP data and theirformats might be different but the overall approach would remain thesame or highly similar and the intended risk factors (risk score, riskranking, patent practice benchmark) will apply to all IP practitionersand IP law firms regardless of the jurisdiction of their practice.

FIG. 2 is an overview of the PPB method 200 divided into 7 stages and 12different sets of processes 201-212, just for the ease of understandingthe method. It should be noted however, that the various processes ofthe method are interconnected and various logical units may access andperform parts of the described processes in a non-exclusive manner.

Stage 1 of the PPB method 200 involves Data Update Process 201 involvingweekly Asset Data updates to be processed the further in the followingstages of the PPB method 200. The Asset Data is available from the USPTOsites such as the Bulk Data Storage System (BDDS), Pair Bulk DataInterface (PBD), or PAIR-Public either as annual back files or regularupdates. The updates are downloaded to their respective databases storedon the local DSD 106 after going through some formatting and clusteringprocesses.

Process 209 of Stage 2 of the PPB method 200 involve Indexing law firmsand patent practitioners and their IP portfolios and consolidating allthe data in one combined PP/LF-Assets Table 406. Process 203 of Stage 2indicate possibility of indexing and consolidating Patent Applicants andAssignees with their IP portfolio in a future embodiment of theinvention.

Stage 3 of the PPB method 200 involves data preparation for furtherprocessing split into three data streams: Transaction History (TH) datapreparation 204, Maintenance Fee Events (MF) data preparation 205, andProfessional Subject Matter Conflict of Interest (CI) data preparation216 resulting in three Market Portfolio & Risk Events Tables for eachstream of data: MP&TH RET 507, MP&MF RET 515, and MP&CI RET 520.

Stage 4 of the PPB method 200 involves risk calculation process splitinto three data streams: TH data processing 207, MF data processing 208,and Professional Subject Matter Conflict of Interest (CI) datapreparation 209 resulting in three Risk Tables for each stream of data:THRT 603, MFRT 609, and CIRT 615. The end results of stage 4 includesalso three risk factor tables (risk score, average risk score,benchmark, benchmark average) for each stream of data: TH risk markers605, MF risk markers 611, and CI risk markers 617.

Process 210 of Stage 5 of the PPB method 200 involves bringing togetherall streams results of stage 4 in a Combined Risk Profile Table (CRPT)702.

Processes 211 of Stage 6 of the PPB method 200 involves categorizing allconsolidated annual risk events per Patent Professional/Law Firmportfolio (CRPT) 702 according to Bloomberg GIC classification resultingin GIC Risk Table (GICRT) 704.

Process 212 of Stage 7 of the PPB method 200 involve advancecalculations and comparisons involving most of the data tables producedduring the process so far to result with the Overall Market Score (OMS)and Market Average Score (MAS) and the Patent Practice Benchmark (PPB)807.

FIG. 3 shows the PPB method 200 data update process 201, according to anexemplary embodiment of the present invention. The initial download ofthe seed data and buildup of the main Patent Grants Data (PGD), PatentApplications Data (PAD), and Transaction History Data (THD) databases isnot shown in FIG. 3 as this is not part of the protection sought forthis invention. Methods to access and download public domain data fromthe USPTO sites are well known in the prior art as disclosed in the U.S.Pat. No. 9,305,278 Patent “System and Methods for compiling IntellectualProperty Asset Data” granted to Gross et all in Apr. 5, 2016 andincorporated by reference herein.

Referring back to FIG. 3, we show the USPTO IPO system 303 and threeUser Interfaces providing access to publicly available data that can beaccessed, identified, and downloaded by the Data Acquisition &Clustering Engine 101 via processes outlined at 307 which differ foreach data source. The data available from USPTO BDSS 304 includes PatentGrants Data (PGD), Published Patent Applications Data (PAD), PatentAssignment XML Data (PASD), Patent Classification Data (CPC, US MCF),Patents Maintenance Fee Events Data (MFD), typically updated weekly formost of these databases. OEDCI 305 hosts the Patent Practitioner Roster(PPR) that is updated daily. The PAIR-Public System 306 allows access toa wide array of IP data arranged under various tabs including theTransaction History Data (THD) and it's updated on weekly bases. Thereare also Third Party Sources of IP-related data 301 that could includepatent docket data, patent analytics data, or patent statistical data.

The Data Acquisition & Clustering Engine 101 may be configured toutilize one or more APIs to data from one or more patent data stores(e.g., public PAIR, private PAIR, INPADOC, foreign patent offices,patent docketing systems, portfolio management systems, etc). TheAPI-targeted data may include published patent documents, patentapplications, office actions or other patent office correspondence,prior art references, claim mappings, dockets dates, annuity paymentdata and patent or patent application assignment information. Specificassignment data may include details pertaining to the assignor orassignee (e.g. name, address, nationality, place of incorporation), dateof assignment, details of the matter being assigned, or any other datapertaining to assignments or change in ownership that may be recorded atany national or regional patent registry such as the USPTO, WIPO or EPO,for example.

An initial download of all IP Asset Data is executed by the DataAcquisition & Clustering Engine 101. That can include active andabandoned files in XML format, which can be downloaded via FTP usingspecific SSL and ETL processes called USPTO Import, by example. Thedownloaded data is stored on a local mass DSD 106. After download, theimported data may be standardized into a common format foraccessibility, speed of processing, or to match the internal data of thePPB system 100 before being stored at their respective databases PGDB,PADB, and THDB 310 on the local and cloud based DSD 106 and 120respectively. Data conditioning may include data rearrangement,normalization, filtering, removing duplicates, sorting, binning, orother operations to transform the data into a common format (e.g., usingsimilar date formats and name formats). The PPR and MFD are cumulativefiles, therefore after their download and processing, they will replacetheir previous version in the database 310.

The THD data for the year 2016 and on has to be scraped and parsed fromthe Pair-Public website since it is not yet available for a download viafile transfer protocol (FTP). First the Data Acquisition & ClusteringEngine 101 extracts the serial numbers (SN) of all patent applicationsand grants from recent PGD, PAD, and MFD downloads and checks themagainst PGDB, PADB, and MFDB to filter out SN of the new events only,saving them in a temporary SQL Hold Table 308 afterwards. Thereafter,the PPB method 200 uses a proprietary human triggered application 309 toaccess the Pair-Public Transaction History Tab for any of the updatedSNs, highlights all of the TH events for chosen SN, copy that data andpaste it into a TH-CSV file to be downloaded and clustered by all thefields into a SQL Hold Table 308. In the next step, the Data Acquisition& Clustering Engine 101 links all of the updated TH events with theirrespective event/action codes as listed in the TH Event Code list(THECL) downloaded from the USPTO. A jurisdiction identifier is alsogenerated to link any of the updated TH events with the SNs of theirrespective patent applications or patent grants. In the final step, theprocessed TH data from the SQL Hold Table 308 is compared with thepermanent THDB 310, which is updated accordingly and stored on the localand cloud based DSD 106 and 120 respectively.

FIG. 4 shows a process 202 of Indexing and consolidating PatentPractitioners and Law Firms by the Data Indexing & Consolidating Engine103. Step 1 of the process involves comparing the full text PGD and PADagainst the PPR to create Master Patent Practitioner Roster Table andMaster Law Firm Roster Table 402 to be saved on the local and cloudbased DSD 106 and 120 respectively. During this step, all patentpractitioners and law firms names in the PDG/PDA are checked against thePPR for possible variations of spelling, events of omission, similarity,duplication, etc. using the 85% conformity rule resulting in an improvedPPR Master List to be used in the further steps of the PPB method 200.The MPPR and MLFR tables are further indexed for single/multi locationand for the number of employed professionals to produce the MPPR-Loc,MLFR-Loc and MLFR-Size tables 402 in steps 3 and 4 and stored on the DSD106 and 120 respectively in step 5. Subsequently, all the 402 Tables arefurther cross-checked against the PGD/PAD to link them with all thepatent applications and patent grants per each patent practitioner orlaw firm per year in step 6. Step 7 consolidates the resulting indexeddata into one portfolio table 406 bringing together the law firm, patentpractitioner, and all their active IP cases on annual bases. Theresulting PP/LF-Assets Table 406 is updated and stored on the local andcloud based DSD 106 and 120 respectively in step 7.

The other stream of PGD/PAD processing 403 involves the Estimator Engine105, which determines one or more factors associated with a patentpractitioner or an IP law firm, including at least the number ofprofessionals, the number of IP transactions conducted per year, and thedollar amount of filing fees paid to the USPTO in a predetermined periodof time and tabulates the results in a MLFR-More Info (MLFR-MI) table404.

FIG. 5A shows the process 204 of Transaction History (TH) datapreparation for further processing involving the Data Filtering Engine102 (steps 1-3), the Data Weighting Engine 104 (steps 4-6), and the DataIndexing & Consolidation Engine 103 (step 7).

Step 1 of the process involves assessing the type and effect of eachevent/action code from the TH Event Code List (THECL) 500 downloadedform the USPTO site, identifying potential liability risk codes andgrouping them by type of errors in the TH Risk Code Table (TH-RCT) 502and storing it on the local and cloud-based DSD 106 and 120respectively. Step 2 involves filtering and profiling the updated THDagainst the pre-selected risk event/action codes from TH-RCT 502 toproduce TH Liability Alert Data (TH-LAD) 503 and storing it to the localand cloud-based DSD 106 and 120 respectively. Step 3 involves extractingthe risk event codes from the TH-LAD 503 for risk event sorted year byyear to produce TH Annual Risk Count Table (TH-ARCT) 505. In step 4 ofthe process, the Data Weighting Engine 104 analyses the seriousness andassigns a weight to each of the event/action codes and adds thatinformation to the TH-RCT 502. Step 5 involves indexing the TH-ARCT 505data by the risk event/action code-weight pairs and storing the updatedTH-RCT 502 and TH-ARCT 505 on the local and cloud-based DSD 106 and 120respectively in step 6. Step 7 is performed by the Data Indexing &Consolidation Engine 103 and involves consolidating the TH-ARCT 505 datawith the PP/LF-Assets 404 portfolio data to create the Market Portfolioand TH Risk Events Table (MP&TH RET) 507 to the local and cloud-basedDSD 106 and 120 respectively.

FIG. 5B shows the process 205 of Maintenance Fee Events (MF) datapreparation for further processing involving the Data Filtering Engine102 (steps 1-3), the Data Weighting Engine 104 (steps 4-6), and the DataIndexing & Consolidation Engine 103 (step 7). Step 1 of the processinvolves assessing the type and effect of each event/action code fromthe MF Event Code List (MFECL) 508 downloaded form the USPTO site,categorising them into groups and subgroups in the MF Event Code Table(MF-ECT) 510 and storing it on the local/cloud DSD 106 and 120respectively. Step 2 involves filtering and profiling the updated MFDagainst the pre-selected event codes from the MF-ECT 510 to produce MFLiability Alert Data (MF-LAD) 511 and storing it to the local andcloud-based DSD DSD 106 and 120 respectively. Step 3 involves extractingthe MF event codes from the MF-LAD 511 for MF events sorted year by yearto produce the MF Annual Event Count Table (MF-AECT) 513. In step 4 ofthe process 512 the Data Weighting Engine 104 analyses the seriousnessand assigns a weight to each of the MF event codes and adds thatinformation to the MF-ECT 510. Step 5 involves indexing the MF-AECT 513data by the event code-weight pairs and storing the updated MF-ECT 510and the MF-AECT 513 on the local and cloud-based DSD 106 and 120respectively in step 6. Step 7 is performed by the Data Indexing &Consolidation Engine 103 and involves consolidating the MF-AECT 513 datawith the PP/LF-Assets Table 404 portfolio data to create the MarketPortfolio and MF Risk Events Table (MP&MF RET) 515 stored to the localand cloud-based DSD 106 and 120 respectively.

FIG. 5C shows the process 206 of Subject Matter Professional Conflict ofInterest (CI) data preparation for further processing involving the DataFiltering Engine 102 (steps 1-8). Step 1 of the process involvesaccessing the updated PGD and PDA from the PGDB and PADB stored on localand cloud-based DSD 106 and 120 respectively. Step 2 involves filteringthe PGD/PAD data to locate patent grants/pending applications associatedwith the same PP/Law Firm and having identical set of patent classes andsub-classes but different entries in the Assignee/Applicant fields. Isstep 3, the total count of potential subject matter conflict events(PSMCE) is divided by 2 to avoid duplication of the same events. In step4, the resulting event count is saved to in a Potential Conflict EventCount Table (CI-ECT) 517. Step 5 involves sorting all PSMCT events yearby year to create Annual CI-ECT (CI-AECT) 518 and storing both tables tothe local and cloud-based DSD 106 and 120 respectively in step 6. Step 7of process 519 involves determining for each PP/Law Firm: a/ totalnumber of PSMCE; b/ number of PSMCE for the current year; c/ number ofPSMCE for the previous years. In step 7, all the data is combined andindexed in the Market Portfolio and Conflict of Interest Risk EventsTable (MP&CI RET) 520 stored to the local and cloud-based DSD 106 and120 respectively.

FIG. 6A shows the process 207 of Transaction History (TH) riskcalculations involving the Data Indexing & Consolidation Engine 103(steps 1-3) and the Data Comparison Engine 107 (steps 4-10). Step 1 ofthe process 600 involves combining the MP&TH RET 507 with the MPPR,MLFR, MPPR-Loc, MLFR-Loc, and MLFR-Size tables 402 to produce a TH DataConsolidation Table (THDCT) 601 containing: a/ law firm names; b/ totalnumber of firm locations (city, state, ZIP code); c/ total number ofpatent practitioners per law firm; d/ total number of patentapplications/grants per law firm within the selected year; e/ totalnumber of applications/grants with at least one pre-selected riskevent/action codes that occurred within the selected year; f/ totalnumber of all pre-selected risk event/action codes that occurred in allapplications/grants within the selected year; g/ total number of eachpre-selected risk event/action codes that occurred within the selectedyear; h/ total number of each pre-selected risk event/action codes thatoccurred within each and all of the selected previous years; i/ highestand most frequent risk event/action codes per year and for all of theprevious years. Step 2 involves repeating steps 1 e-i for all groups ofTH risk event/action codes. Step 3 involves storing the THDCT 601 on thelocal and cloud-based DSD 106 and 120 respectively.

In step 4 of the process 602, the Data Comparison Engine 107 performsscoring of the TH risk event/action codes and their frequency ofoccurrence by accessing the THDCT 601 and performing the followingcalculations and data comparisons: a/ calculating the percentage ofapplications/grants that have pre-selected risk event/action codes thatoccurred in the selected year to total number of risk/event actioncodes; b/ calculating the ratio of occurrence of pre-selected riskevent/action codes per application/grant for each Law Firm; c/calculating the rate of occurrence of pre-selected risk event/actioncodes per application/grant for each Law Firm (how often and at whattimes apart). Step 5 of the process 602 involves tabulating all of theresults in the TH-Risks Table (THRT) 603 and storing it on the local andcloud-based DSD 106 and 120 respectively.

Step 6 of the process 604 involves the Data Comparison Engine 107calculating the TH Score (THS) for each of the Law Firms based onevaluation of the THRT 603 data and ranking the Law Firms according totheir THS. Step 7 involves calculating TH Average Score (THSA) based onprevious calculations. Step 8 involves selecting all Law Firms with THShigher than the THSA. Step 9 involves selecting 20 top-scoring Law Firmsthat have: a/ the highest number of portfolio cases (pendingapplications/grants); b/ the lowest number of pre-selected riskevent/action codes; c/ the lowest ratio of pre-selected riskevent/action codes to total number of portfolio cases as the industry THBenchmark (THB). Step 10 involves tabulating the above markers andstoring them in the TH Markers Table (THMT) 605 on the local andcloud-based DSD 106 and 120 respectively.

FIG. 6B shows the process 208 of Maintenance Fee Events (MF) riskcalculations involving the Data Indexing & Consolidation Engine 103(steps 1-3) and the Data Comparison Engine 107 (steps 4-10). Step 1 ofthe process 606 involves combining the MP&MF RET 515 with the MPPR,MLFR, MPPR-Loc, MLFR-Loc, and MLFR-Size tables 402 to produce a MF DataConsolidation Table (MFDCT) 607 containing: a/ law firm names; b/ totalnumber of firm locations (city, state, ZIP code); c/ total number ofpatent practitioners per law firm; d/ total number of patentapplications/grants per law firm within the selected year; e/ totalnumber of applications/grants with at least one pre-selected MFevent/action code that occurred within the selected year; f/ totalnumber of all pre-selected MF event/action codes that occurred in allapplications/grants within the selected year; g/ total number of eachpre-selected MF event/action codes that occurred within the selectedyear; h/ total number of each pre-selected MF event/action codes thatoccurred within each and all of the selected previous years; i/ highestand most frequent MF event/action code per year and for all of theprevious years. Step 2 involves repeating steps 1 e-i for all groups ofMF event/action codes. Step 3 involves storing the MFDCT 607 on thelocal and cloud-based DSD 106 and 120 respectively.

In step 4 of the process 608, the Data Comparison Engine 107 performsscoring of the MF event/action codes and their frequency of occurrenceby accessing the MFDCT 607 and performing the following calculations anddata comparisons: a/ calculating the percentage of applications/grantsthat have pre-selected MF event/action codes that occurred in theselected year to total number of MF event action codes; b/ calculatingthe ratio of occurrence of pre-selected MF event/action codes perapplication/grant for each Law Firm; c/ calculating the rate ofoccurrence of pre-selected MF event/action codes per application/grantfor each Law Firm (how often and at what times apart). Step 5 of theprocess 608 involves tabulating all of the results in the MF-Risks Table(MFRT) 609 and storing it on the local and cloud-based DSD 106 and 120respectively.

Step 6 of the process 610 involves the Data Comparison Engine 107calculating the MF Score (MFS) for each of the Law Firms based onevaluation of the MFRT 609 data and ranking the Law Firms according totheir MFS. Step 7 involves calculating MF Average Score (MFSA) based onprevious calculations. Step 8 involves selecting all Law Firms with MFShigher than the MFSA. Step 9 involves selecting 20 top-scoring Law Firmsthat have: a/ the highest number of portfolio cases (pendingapplications/grants); b/ the lowest number of pre-selected MFevent/action codes; c/ the lowest ratio of pre-selected MF event/actioncodes to total number of portfolio cases as the industry MF Benchmark(MFB). Step 10 involves tabulating the above markers and storing them inthe MF Markers Table (MFFT) 611 on the local and cloud-based DSD 106 and120 respectively.

FIG. 6C shows the process 209 of Subject Matter Professional Conflict ofInterest (CI) risk calculations involving the Data Indexing &Consolidation Engine 103 (steps 1-3) and the Data Comparison Engine 107(steps 4-10). Step 1 of the process 612 involves combining the MP&CI RET520 with the MPPR, MLFR, MPPR-Loc, MLFR-Loc, and MLFR-Size tables 402 toproduce a CI Data Consolidation Table (CIDCT) 613 containing: a/ lawfirm names; b/ total number of firm locations (city, state, ZIP code);c/ total number of patent practitioners per law firm; d/ total number ofpatent applications/grants per law firm within the selected year; e/total number of applications/grants with at least one CI risk event thatoccurred within the selected year; f/ total number of all CI risk eventsthat occurred in all applications/grants within the selected year; g/total number of each CI risk event that occurred within the selectedyear; h/ total number of each pre-selected CI risk event that occurredwithin each and all of the selected previous years; i/ highest and mostfrequent CI risk event per year and for all of the previous years. Step2 involves repeating steps 1 e-i for all groups of CI risk events. Step3 involves storing the CIDCT 613 on the local and cloud-based DSD 106and 120 respectively.

In step 4 of the process 614, the Data Comparison Engine 107 performsscoring of the CI risk events and their frequency of occurrence byaccessing the CIDCT 613 and performing the following calculations anddata comparisons: a/ calculating the percentage of applications/grantsthat have pre-selected CI risk event that occurred in the selected yearto total number of CI risk events; b/ calculating the ratio ofoccurrence of pre-selected CI event per application/grant for each LawFirm; c/ calculating the rate of occurrence of CI risk events perapplication/grant for each Law Firm (how often and at what times apart).Step 5 of the process 614 involves tabulating all of the results in theCI-Risks Table (CIRT) 615 and storing it on the local and cloud-basedDSD 106 and 120 respectively.

Step 6 of the process 616 involves the Data Comparison Engine 107calculating the CI Score (CIS) for each of the Law Firms based onevaluation of the CIRT 615 data and ranking the Law Firms according totheir CIS. Step 7 involves calculating CI Average Score (CISA) based onprevious calculations. Step 8 involves selecting all Law Firms with CIShigher than the CIAS. Step 9 involves selecting 20 top-scoring Law Firmsthat have: a/ the highest number of portfolio cases (pendingapplications/grants); b/ the lowest number of pre-selected CI riskevents; c/ the lowest ratio of pre-selected CI risk event to totalnumber of portfolio cases as the industry CI Benchmark (CIB). Step 10involves tabulating the above markers in the CI Markers Table (CIFT) 617and storing it on the local and cloud-based DSD 106 and 120respectively.

FIG. 7 shows stage 5 process 210 of combining all the results from stage4 by the Data Indexing & Consolidation Engine 103 (steps 1-2) and stage6 process 211 Bloomberg GIC grouping of the combined portfolio risk databy industry groups done by the Data Indexing & Consolidation Engine 103(steps 1-3).

Step 1 of the process 701 involves consolidating the data from THRT 603.MFRT 609, and CIRT 615 into one Combined Risk Profile Table (CRPT) 702sorted by years and containing: a/ law firm names; b/ total number offirm locations (city, state, ZIP code); c/ total number of patentpractitioners per Law Firm; d/ total number of applications/grants perlaw firm within the selected year; e/ total number of risk events/actioncodes that occurred in all applications/grants within the selected year;f/ total number of applications/grants with at least one riskevent/action code; g/ total number of each pre-selected riskevent/action code that occurred within the selected year; h/ totalnumber of each pre-selected event/action code that occurred within eachand all of the selected previous years; i/ total number of riskevents/action codes per group/category; j/ highest & most frequent riskevent/action code per year and for all of the previous years; k/correlation of risk events between TH/MF/CI risk events/action codesagainst each other (comparing all tables against each other). Step 2 ofthe process 701 involves tabulating the results in the Combined RiskProfile Table (CRPT) 702 and storing it on the local and cloud-based DSD106 and 120 respectively.

Process 211 of stage 6 consists of 3 steps done by the Data Indexing &Consolidation Engine 103. Step 1 of the process 211 involves matchingBloomberg GIC codes with the USPTO patent class codes to categorize CRPT702 data into the following Subject Matter & Industry Groups: Energy,Materials, Health Care, Industrials, Consumers, Finances, Utilities,Telecommunications, and Information Technology. Step 2 involves indexingthe pre-selected risk event/action codes within each group to determinethe quantity and distribution of the risk event/action codes for eachLaw Firm, by the following markers: a/ total number of pre-selectedeven/action codes; b/ total number of applications/grants with at leastone pre-selected event/action code; c/ how many times each pre-selectedevent/action code occurred within the selected year; d/ how many timeseach pre-selected event/action code occurred within the previous years.Step 3 involves tabulating the results into GIC Risk Table (GICRT) 704and storing it on the local and cloud-based DSD 106 and 120respectively.

FIG. 8 shows the final process 212 of stage 7 of performing the advancedcalculations and data comparisons by the Data Comparison Engine 107(steps 1-9) to arrive at the Overall Market Score (OMS), the OverallMarket Average (OMA), and the Patent Practice Benchmark (PPB) 807 to beused for comparison analyses query or for periodic liability riskanalysis reports.

Step 1 of process 801 involves calculating average of the TH, MF, and CIFactors from the TH Factors Table 605 (RHS, THAS, THB, THBA), MF FactorsTable 611 (MFS, MFAS, MFB, MFBA), and CI Factors Table 617 (CIS, CIAS,CIB, CIBA) to arrive at Overall Market Score (OMS) and Market AverageScore (MAS) and storing them in the Overall Market Factors Table (OMFT)802 DSD 106 and 120 respectively at step 2.

Step 3 of process 804 involves selecting all Law Firms with OMS higherthan the MAS. Step 4 involves selecting 20 top-scoring Law Firms asPatent Practice Benchmark (PPB) 807 that have: a/ the highest number ofportfolio cases (applications/grants); b/ the lowest number ofpre-selected risk event/action codes; c/ the lowest ratio ofpre-selected risk event/action codes to total number of portfolio cases.Step 5 involves choosing data from the CRPT 702 and GICRT 704 related totwo consecutive year periods (i.e. A=last year, B=current year) todetermine the following: a. How many firms moved up within the rankingof the Top 50-100 firms (Year A to B); b. How many firms dropped fromYear A to B within the Top 50-100 firms; c. The average of growth ratein each portfolio (cases per year); d. The average rate and growth rateof the TH risk event/action codes occurrence; e. The average growth rateof the MF risk events occurrence; f. The average of ratio of additionalfactors (i.e. area of practice, GLT location, frequency of risk events,other) for the risk event/action codes per case. Step 6 involvestabulating all of the results in a transitory Risk Growth Table (RGT)805 stored in the local and cloud-based DSD 106 and 120 respectively.Step 7 involves determining based on the RGT 805 the Patent PracticeBenchmark (PPB) 807 for Law Firms ranked by size, single/multiple officelocations, and area of practice, the choice of PPB 807 based on a LawFirm having: a/ the highest growth rate in portfolio; b/ the lowest rateand growth rate of risk event/action codes; c/ the lowest occurrencerate and growth rate of risk event/action codes per application/patent;d/ highest profitability ratio (number of payments made in one year fora given portfolio to the number of patent practitioners, and what arethe most common type of payments. Step 8 of the process 806 involvesselecting the best 20 matches from the previous analysis as the currentyear Patent Practice Benchmark (PPB) 807, which is stored in the localand cloud-based DSD 106 and 120 respectively.

As the PPB method 200 data processing and risk calculations are resourceand time demanding, and they require on-going updates of the Asset Data,the PPB method 200 analysis and calculations are done in advance. Therisk factors are being updated on regular bases (i.e. after each AssetData update) and stored in the PPB 100 system as a pre-calculated dataready to be used at any time. A user 808 can log in to the PPB System100 via Network 130 (the Internet in the preferred embodiment) and inputa query for a liability risk or liability insurance risk analysis for apre-selected law firm (Target Entity), upon which the PPB system 100will output the corresponding risk factors and other relevant datadepending on the user 808 access privileges.

Each user of PPB System 100 will be required to create an accountproviding his/her name, company name, address, phone number and email aswell as disclosure whether he/she is a law firm, insurance company, or ageneral client. This identification type, if approved, will determinethe user's display (law firm/insurance firm/general client) once theaccount is activated. The user must select a UserID (email orcombination of choice) and a password (combination of choice). Once thisrequest is submitted. The account request will be reviewed by an accountmanager who will assess client's requirements and upon approval theaccountant will be activated.

FIG. 9 shows three exemplary dashboards of a user-dependant GUI of thePPB computer-based system 100: an Insurance Firm Dashboard 901, IP LawFirm Dashboard 902, and a General Client Dashboard 903. Graphically, thedashboard layouts are similar and feature the same kind of buttons 904,905, 906, and risk performance gauges 907, which graphically show theuser's risk score (Quotia Score) in relation to the PPB 807. The onlydifference is in the range of access information provided to each userbased on its access privileges, which would be detected automaticallyafter log in. Those GUI examples are shown for introductory reasonsalone and make take entirely different visual and functional format uponactual launch of the PPB System 100.

General clients will see a dashboard 903 having only the basic riskinformation (Overall Market Score (OMS) and Market Average Score (MAS)802 as well as PPB 807) allowing them to have a basic overview of theliability profile of the IP industry market. Upon request, they may begiven access to more information about individual law firms allowingthem to have an informed choice of patent professionals or law firmsaccording to their IP protection needs.

Law firms will see a dashboard 902 showing their calendar year riskresult tables, charts, and gauges and a notification panel 908 allowingthem to request notification if any current year events display certaintypes of risk events. The risk factors available to the law firmsdepending upon their requirements and subject to their subscriptionpackages might include: Quotia Score, Quotia Score firm size by agentvs. Benchmark, Quotia Score firm size by portfolio size vs. Benchmark,Quotia Score firm size by agent vs. other firms of the same size, QuotiaScore firm size vs. other firms of the same size in a chosen state,Quotia Score firm size vs. other firms of the same size in the overallmarket, breakdown of payment history vs. Benchmark, breakdown of paymenthistory vs. other firms of the same size, breakdown of payment historyvs. other firms in the same state, breakdown of payment history vs.other firms in the same technology area and the same state, breakdown ofrisk errors by category, breakdown of errors by category comparison vs.Benchmark, breakdown of errors by category comparison vs. other firms ofthe same size, breakdown of errors by category comparison vs. otherfirms in the same state, breakdown of errors by frequency of occurrence,breakdown of frequency of occurrence by category of error, breakdown offrequency of occurrence vs. Benchmark, breakdown of frequency ofoccurrence by category of error vs. Benchmark, breakdown of potentialTrademark priority risk profile, if applicable showing risk fromCanadian associates filing applications for applicants represented bythe USPTO firm/attorney, trend of each of these reports compared to lastyear up/or down movements, and others. The results can be printed andsent via email.

Insurance firms, in addition to the law firm information, will have themost risk information choices available on the screen, includingpossibility of having direct access to multiple law firms' dashboards(“one-click approach”) on which they will see insurance policy numbers,policy renewal dates, and comparison of a policyholder risk rating tothe established PPB Benchmark 807, as well as an array of charts withvarious risk factors and comparisons as provided by the PPB System 100.They will have also access to a Market & Analysis Panel/Dashboard andPolicy Holder RiskInsight Panel/Dashboard. The Market & AnalysisPanel/Dashboard can include market overview of errors, location oferrors, types of technology, types of portfolio size, types of firmsize, types of frequency and others. The Policy Holder RiskInsightPanel/Dashboard can have the following features: a/ ability to addlimited but multiple law firms based on subscription fee paid and trackbased on the same results; b/ additional firms can be added ifsubscription fee is paid; c/ ability to compare scores and results offirms in insurance portfolio to Benchmark; d/ chart showing risk ofoccurrence/claims vs. Benchmark by firm and overall insurance portfolio;e/ breakdown of risk events by insurance portfolio vs. Benchmark; f/ability to select policy renewal date; g/ chart of risk events sincelast policy renewal by firm and overall insurance portfolio; h/ chart ofrisk potential reportable claims by firm and overall insuranceportfolio; i/ graph of Newbridge results vs. Benchmark; j/ chart ofpremiums risk identification by firm and overall insurance portfolio; k/potential liability for prior acts by firm and overall insuranceportfolio; l/ breakdown of serious errors in the last 3 years; and m/detailed subject matter conflict analysis.

In the preceding description, numerous details are set forth in order toprovide a thorough understanding of the embodiments. However, it will beapparent to one skilled in the art that these specific details are notrequired. In other instances, well-known electrical structures andcircuits are shown in block diagram form in order not to obscure theunderstanding. For example, specific details are not provided as towhether the embodiments described herein are implemented as a software,hardware circuit, firmware, or a combination thereof.

Embodiments of the disclosure can be represented as a computer programproduct stored in a machine-readable medium (also referred to as acomputer-readable medium, a processor-readable medium, or a computerusable medium having a computer-readable program code embodied therein).The machine-readable medium can be any suitable tangible, non-transitorymedium, including magnetic, optical, or electrical storage mediumincluding a diskette, compact disk read only memory (CD-ROM), memorydevice (volatile or non-volatile), or similar storage mechanism. Themachine-readable medium can contain various sets of instructions, codesequences, configuration information, or other data, which, whenexecuted, cause a processor to perform steps in a method according to anembodiment of the disclosure. Those of ordinary skill in the art willappreciate that other instructions and operations necessary to implementthe described implementations can also be stored on the machine-readablemedium. These instructions can be executed by a processor or othersuitable processing device, and can interface with circuitry to performthe described tasks.

The above-described embodiments are intended to be examples only.Alterations, modifications and variations can be effected to theparticular embodiments by those skilled in the art without departingfrom the scope of present invention, which is defined solely by theclaims appended hereto.

What is claimed is:
 1. A computer-implemented method for detecting, profiling and benchmarking liability trigger events indicative of performance quality and subject matter conflict of interest of intellectual property (IP) professional practices and susceptible to affect a liability risk profile and a liability insurance risk profile of a target entity engaged in said IP professional practices, including IP professionals employed or representing said target entity, the method comprising: a. using one or more processors to execute first set of computer-executable statements and instructions for communicating electronically with a National/Regional Intellectual Property Office (IPO) computer system in at least one IP jurisdiction, identifying and extracting Asset Data from said IPO computer system, processing and clustering said extracted Asset Data, generating an IP jurisdiction identifier to associate each document of said Asset Data with the jurisdiction of the corresponding IP document, and storing said IP jurisdiction identifier with said each document of said Asset Data therewith to a data storage device; b. using one or more processors to execute second set of computer-executable statements and instructions for indexing and consolidating said Asset Data and selective internal results of calculations and comparisons performed on said Asset Data, and storing the indexed and/or consolidated Asset Data and said selective internal results of calculations and comparisons performed on said Asset Data to said data storage device. c. using one or more processors to execute third set of computer-executable statements and instructions for filtering and profiling the processed Asset Data by checking for codes associated with one or more of said liability trigger events indicative of said performance quality and said subject matter conflict of interest of IP professional practices and susceptible to affect said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, to produce Liability Alert Data, and storing said Liability Alert Data to said data storage device; d. using one or more processors to execute fourth set of computer-executable statements and instructions for applying one or more predetermined factors associated with said one or more liability trigger events to said Liability Alert Data to produce Weighted Liability Alert Data, and storing said Weighted Liability Alert Data to said data storage device; e. using one or more processors to execute fifth set of computer-executable statements and instructions to determine, based on receiving said processed Asset Data from said storage device, one or more factors associated with said target entity, including at least the number of professional employees, the number of IP transaction conducted, and the dollar amount of filing fees paid in a pre-determined period of time, and to output the resulting information to said data storage device, and/or to a user interface, and/or to a display device. f. using one or more processors to execute sixth set of computer-executable statements and instructions to determine said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, by applying one or more predictive models trained on said Asset Data to said Weighted Liability Alert Data, and outputting said liability risk profile and said liability insurance risk profile to said data storage device, and/or to a user interface, and/or to a display device.
 2. The computer-implemented method of claim 1, wherein said subject matter conflict of interest of said IP professional practices of said target entity, is determined by using one or more processors to execute seventh set of computer executable statements and instructions, the method comprising: a. filtering and profiling said processed Asset Data to find all IP documents associated with the same target entity, including IP professionals employed or representing said target entity, and outputting the resulting documents to said data storage device, and/or to said user interface, and/or to said display device; b. filtering and profiling further said all IP documents associated with the same target entity, including IP professionals employed or representing said target entity, to find IP documents having identical set of classification classes and subclasses, and outputting the resulting documents to said data storage device, and/or to said user interface, and/or to said display device; c. filtering and profiling further said IP documents having identical set of classification classes and subclasses to find IP documents having different entries in the Applicant field or in the Assignee field respectively, and outputting the resulting documents to said data storage device, and/or to said user interface, and/or to said display device.
 3. The computer-implemented method of claim 1, wherein said at least one IP jurisdiction is administered by the United States Patent and Trademark Office (USPTO).
 4. The computer-implemented method of claim 1, wherein said Asset Data comprises at least the IP prosecution transaction data inclusive of the Patent Information Retrieval System (PAIR) data and the IP administration data inclusive of the post-grant administration data.
 5. The computer-implemented method of claim 1, wherein said one or more IP liability trigger events comprise one or more occurrences of professional practice errors, omissions, events of incompleteness, delay, oversight or mismanagement by said target entity, including IP professionals employed or representing said target entity.
 6. The computer-implemented method of claim 1, wherein said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, comprises at least frequency of occurrence and probability of re-occurrence of said liability trigger events.
 7. The computer-implemented method of claim 1, wherein said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, is determined in one or more predetermined categories including at least area of IP practice, size of IP portfolio, the number of professional employees, types of subject matter, geographical location, and the number of offices of said target entity.
 8. The computer-implemented method of claim 1, wherein said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, is monitored in real-time.
 9. The computer-implemented method of claim 1, wherein said liability risk profile of said target entity, including IP professionals employed or representing said target entity, includes a liability risk score of said target entity; and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, includes a liability insurance risk score of said target entity.
 10. The computer-implemented method of claim 9, wherein said liability risk profile of said target entity, including IP professionals employed or representing said target entity, includes a liability risk ranking based on said liability risk score of said target entity; and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, includes a liability insurance risk ranking based on said liability insurance risk score of said target entity.
 11. The computer-implemented method of claim 10, wherein said liability risk ranking and said liability insurance risk ranking of said target entity, including IP professionals employed or representing said target entity, comprise one or more predetermined benchmarks, based on business entities having the highest liability risk score in said one or more predetermined categories, to rank said target entity against said one or more predetermined benchmarks.
 12. The computer-implemented method of claim 1, wherein said user interface comprises a general client module, a law firm module, and an insurance firm module.
 13. The computer-implemented method of claim 12, wherein said general client module comprises at least said liability risk score and said liability risk ranking of said target entity, including IP professionals employed or representing said target entity, allowing clients to lower risks associated with choosing an IP law firm or an independent IP service provider.
 14. The computer-implemented method of claim 12, wherein said law firm module comprises at least said liability risk score and said liability risk ranking of IP professionals employed or representing said target entity, allowing law firms to lower liability risks associated with said IP professional practices and susceptible to affect their human resources practices.
 15. The computer-implemented method of claim 12, wherein said insurance firm module comprises at least said liability risk score and said liability risk ranking of said target entity, including IP professionals employed or representing said target entity, allowing insurance firms to mitigate risks associated with providing a liability insurance coverage to said target entity.
 16. A computer-based system for detecting, profiling and benchmarking liability trigger events indicative of performance quality and subject matter conflict of interest of intellectual property (IP) professional practices and susceptible to affect liability risk profile and liability insurance risk profile of a target entity engaged in said IP professional practices, including IP professionals employed or representing said target entity, the system comprising: a. a data acquisition & clustering engine configured to use one or more processors to execute first set of computer-executable statements and instructions for communicating electronically with a National/Regional Intellectual Property Office (IPO) computer system in at least one IP jurisdiction, identifying and extracting Asset Data from said IPO computer system, processing and clustering said extracted Asset Data, generating an IP jurisdiction identifier to associate each document of said Asset Data with the jurisdiction of the corresponding IP document, and storing said IP jurisdiction identifier with said each document of said Asset Data therewith to a data storage device; b. a data indexing & consolidating engine configured to use one or more processors to execute second set of computer-executable statements and instructions for indexing and consolidating said Asset Data and selective internal results of calculations and comparisons performed on said Asset Data, and storing the indexed and/or consolidated Asset Data and said selective internal results of calculations and comparisons performed on said Asset Data to said data storage device; c. a data filtering engine configured to use one or more processors to execute third set of computer-executable statements and instructions for filtering and profiling the processed Asset Data by checking for codes associated with one or more of said liability trigger events indicative of performance quality and subject matter conflict of interest of IP professional practices and susceptible to affect liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, to produce Liability Alert Data, and storing said Liability Alert Data to said data storage device; d. a data weighting engine configured to use one or more processors to execute fourth set of computer-executable statements and instructions for applying one or more predetermined factors associated with said one or more liability trigger events to said Liability Alert Data to produce Weighted Liability Alert Data, and storing said Weighted Liability Alert Data to said data storage device; e. an estimator engine configured to use one or more processors to execute fifth set of computer executable statements and instructions to determine, based on receiving said processed Asset Data from said storage device, one or more factors associated with said target entity, including at least the number of professional employees, the number of IP transaction conducted, and the dollar amount of filing fees paid in a pre-determined period of time, and to output the resulting information to said data storage device, and/or to a user interface, and/or to a display device; f. a data comparison engine configured to use one or more processors to execute sixth set of computer executable statements and instructions to determine said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, by applying one or more predictive models trained on said Asset Data to said Weighted Liability Alert Data, and outputting said liability risk profile and said liability insurance risk profile to said data storage device, and/or to said user interface, and/or to said display device; said data comparison engine configured further to use one or more processors to execute seventh set of computer executable statements and instructions to determine said subject matter conflict of interest of IP professional practices of said target entity by filtering and profiling said processed Asset Data to find all documents associated with the same target entity, filtering and profiling further the results to find documents having identical set of classification classes and subclasses, filtering and profiling further the results to find documents having different entries in the Applicant field or in the Assignee field respectively, and outputting the resulting documents to said data storage device, and/or to said user interface, and/or to said display device; g. a data storage device comprising at least one database configured to store at least one set of computer executable statements and instructions, said data storage device configured further to receive and store said Asset Data from said IPO computer system in at least one IP jurisdiction and third party sources data; said data storage device configured further to receive and store all internal data of said computer-based system; h. a user interface configured to allow for a two-way communication between said computer-based system and at least one user of said computer-based system, including receiving an input from said at least one user and displaying information received from said computer-based system in response to said input from said at least one user; i. a search engine configured to use one or more processors to execute eighth set of computer-executable statements and instructions to receive an input from said at least one user via said user interface and to automatically retrieve corresponding information from said computer-based system in response to said at least one user input; said search engine configured further to output said corresponding information to said user interface; j. communication means, configured to enable a two-way communication between said computer-based system and said at least one user via said user interface; said communication means configured further to allow for data transfer from said IPO computer system in at least one IP jurisdiction and from third-party data sources.
 17. The computer-based system of claim 16, wherein said at least one IP jurisdiction is administered by the United States Patent and Trademark Office (USPTO).
 18. The computer-based system of claim 16, wherein said Asset Data comprises at least IP prosecution transaction data inclusive of the Patent Information Retrieval System (PAIR) data and the IP administration data inclusive of the post-grant administration data.
 19. The computer-based system of claim 16, wherein said one or more IP liability trigger events comprise one or more occurrences of professional practice errors, omissions, events of incompleteness, delay, oversight or mismanagement by said target entity, including IP professionals employed or representing said target entity.
 20. The computer-based system of claim 16, wherein said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, comprises at least frequency of occurrence and probability of re-occurrence of said IP liability trigger events.
 21. The computer-based system of claim 16, wherein said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, is determined in one or more predetermined categories including at least area of IP practice, size of IP portfolio, the number of professional employees, types of subject matter, geographical location, and the number of offices of said target entity.
 22. The computer-based system of claim 16, wherein said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, is monitored in real-time.
 23. The computer-based system of claim 16, wherein said liability risk profile of said target entity, including IP professionals employed or representing said target entity, includes a liability risk score of said target entity; and said liability insurance risk profile of said target entity includes liability insurance risk score of said target entity.
 24. The computer-based system of claim 23, wherein said liability risk profile of said target entity, including IP professionals employed or representing said target entity, includes a liability risk ranking based on said liability risk score of said target entity; and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, includes a liability insurance risk ranking based on said liability insurance risk score of said target entity.
 25. The computer-based system of claim 24, wherein said liability risk ranking and said liability insurance risk ranking of said target entity, including IP professionals employed or representing said target entity, comprise one or more predetermined benchmarks, based on business entities having the highest liability risk score in said one or more predetermined categories, to rank said target entity against said one or more predetermined benchmarks.
 26. The computer-based system of claim 16, wherein said user interface comprises a general client module, a law firm module, and an insurance firm module.
 27. The computer-based system of claim 26, wherein said general client module, comprises at least said liability risk score and said liability risk ranking of said target entity, including IP professionals employed or representing said target entity, allowing clients to lower risks associated with choosing an IP law firm or an independent IP service provider.
 28. The computer-based system of claim 26, wherein said law firm module, comprises at least said liability risk score and said liability risk ranking of IP professionals employed or representing said target entity, allowing law firms to lower liability risks associated with said IP professional practices and susceptible to affect their human resources practices.
 29. The computer-based system of claim 26, wherein said insurance firm module, comprises at least said liability risk score and said liability risk ranking of said target entity, including IP professionals employed or representing said target entity, allowing insurance firms to mitigate risks associated with providing a liability insurance coverage to said target entity.
 30. A non-transitory computer-readable medium having computer executable statements and instructions stored thereon, said executable statements and instructions adapted to be executed by one or more processors of a computer-based system for detecting, profiling and benchmarking liability trigger events indicative of performance quality and subject matter conflict of interest of intellectual property (IP) professional practices and susceptible to affect liability risk profile and liability insurance risk profile of a target entity engaged in said IP professional practices, including IP professionals employed or representing said target entity, said executable statements and instructions comprising: a. first set of computer-executable statements and instructions for communicating electronically with a National/Regional Intellectual Property Office (IPO) computer system in at least one IP jurisdiction, identifying and extracting Asset Data from said IPO computer system, processing and clustering said extracted Asset Data, generating an IP jurisdiction identifier to associate each document of said Asset Data with the jurisdiction of the corresponding IP document, and storing said IP jurisdiction identifier with said each document of said Asset Data therewith to a data storage device; b. second set of computer-executable statements and instructions for indexing and consolidating said Asset Data and selective internal results of calculations and comparisons performed on said Asset Data, and storing the indexed and/or consolidated Asset Data and said selective internal results of calculations and comparisons performed on said Asset Data to said data storage device; c. third set of computer-executable statements and instructions for filtering and profiling the processed Asset Data by checking for codes associated with one or more of said liability trigger events indicative of said performance quality and said subject matter conflict of interest of IP professional practices and susceptible to affect said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, to produce Liability Alert Data, and storing said Liability Alert Data to said data storage device; d. fourth set of computer-executable statements and instructions for applying one or more predetermined factors associated with said one or more liability trigger events to said Liability Alert Data to produce Weighted Liability Alert Data, and storing said Weighted Liability Alert Data to said data storage device; e. fifth set of computer-executable statements and instructions to determine, based on receiving said processed Asset Data from said storage device, one or more factors associated with said target entity, including at least the number of professional employees, the number of IP transaction conducted, and the dollar amount of filing fees paid in a pre-determined period of time, and to output the resulting information to said data storage device, and/or to a user interface, and/or to a display device; f. sixth set of computer-executable statements and instructions to determine said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, by applying one or more predictive models trained on said Asset Data to said Weighted Liability Alert Data, and outputting said liability risk profile and said liability insurance risk profile to said data storage device, and/or to said user interface, and/or to said display device; g. seventh set of computer executable statements and instructions to determine said subject matter conflict of interest of IP professional practices of said target entity by filtering and profiling said processed Asset Data to find all documents associated with the same target entity, filtering and profiling further the results to find documents having identical set of classification classes and subclasses, filtering and profiling further the results to find documents having different entries in the Applicant field or in the Assignee field respectively, and outputting the resulting documents to said data storage device, and/or to said user interface, and/or to said display device; h. eighth set of computer-executable statements and instructions to receive an input from at least one user via user interface and to automatically retrieve corresponding information from said computer-based system in response to said at least one user input, and to output said corresponding information to said user interface.
 31. A non-transitory computer-readable medium of claim 30 having computer executable statements and instructions stored thereon, wherein said at least one IP jurisdiction is administered by the United States Patent and Trademark Office (USPTO).
 32. A non-transitory computer-readable medium of claim 30 having computer executable statements and instructions stored thereon, wherein said Asset Data comprises at least the IP prosecution transaction data inclusive of the Patent Information Retrieval System (PAIR) data and the IP administration data inclusive of the post-grant administration data.
 33. A non-transitory computer-readable medium of claim 30 having computer executable statements and instructions stored thereon, wherein said one or more liability trigger events comprise one or more occurrences of professional practice errors, omissions, events of incompleteness, delay, oversight or mismanagement by said target entity, including IP professionals employed or representing said target entity.
 34. A non-transitory computer-readable medium of claim 30 having computer executable statements and instructions stored thereon, wherein said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, comprises at least frequency of occurrence and probability of re-occurrence of said liability trigger events.
 35. A non-transitory computer-readable medium of claim 30 having computer executable statements and instructions stored thereon, wherein said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, is determined in one or more predetermined categories including at least area of IP practice, size of IP portfolio, the number of professional employees, types of subject matter, geographical location, and the number of offices of said target entity.
 36. A non-transitory computer-readable medium of claim 30 having computer executable statements and instructions stored thereon, wherein said liability risk profile and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, is monitored in real-time.
 37. A non-transitory computer-readable medium of claim 30 having computer executable statements and instructions stored thereon, wherein said liability risk profile of said target entity, including IP professionals employed or representing said target entity, includes a liability risk score of said target entity; and said liability insurance risk profile of said target entity, including IP professionals employed or representing said target entity, includes liability insurance risk score of said target entity.
 38. A non-transitory computer-readable medium of claim 37 having computer executable statements and instructions stored thereon, wherein said liability risk profile of said target entity, including IP professionals employed or representing said target entity, includes a liability risk ranking based on said liability risk score of said target entity; and said liability insurance risk profile of said target entity includes liability insurance risk ranking based on said liability insurance risk score of said target entity.
 39. A non-transitory computer-readable medium of claim 38 having computer executable statements and instructions stored thereon, wherein said liability risk ranking and said liability insurance risk ranking of said target entity, including IP professionals employed or representing said target entity, comprise one or more predetermined benchmarks, based on business entities having the highest liability risk score in said one or more predetermined categories, to rank said target entity against said one or more predetermined benchmarks.
 40. A non-transitory computer-readable medium of claim 30 having computer executable statements and instructions stored thereon, wherein said user interface comprises a general client module, a law firm module and an insurance firm module.
 41. A non-transitory computer-readable medium of claim 40 having computer executable statements and instructions stored thereon, wherein said client module comprises at least said liability risk score and said liability risk ranking of said target entity, including IP professionals employed or representing said target entity, allowing clients to lower risks associated with choosing an IP law firm or an independent IP service provider.
 42. A non-transitory computer-readable medium of claim 40 having computer executable statements and instructions stored thereon, wherein said law firm module, comprises at least said liability risk score and said liability risk ranking of IP professionals employed or representing said target entity, allowing law firms to lower liability risks associated with said IP professional practices and susceptible to affect their human resources practices.
 43. A non-transitory computer-readable medium of claim 40 having computer executable statements and instructions stored thereon, wherein said insurance firm module, comprises at least said liability risk score and said liability risk ranking of said target entity, including IP professionals employed or representing said target entity, allowing insurance firms to mitigate risks associated with providing a liability insurance coverage to said target entity. 